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      <title>消息的顺序保证 - 交互式测验</title>
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    </head>
    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>消息的顺序保证 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
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                    <div id="question-container">
                      <p id="question-text"></p>
                      <ul class="options-list" id="options-list"></ul>
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                    <div id="result-container"></div>
                    <div id="hint-container" class="hint-container"></div>
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                <div class="quiz-footer" id="quiz-footer">
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      <script>
        const quizData = [
  {
    "question": "根据文档，对Kafka消息顺序性保证最准确的描述是什么？",
    "options": [
      "Kafka保证一个Topic内的所有消息都是全局有序的。",
      "Kafka保证在单个Partition内的消息是有序的，但不能保证跨Partition的消息顺序。",
      "Kafka保证由同一个Producer发送的所有消息都是全局有序的。",
      "Kafka只有在使用单个消费者时才能保证消息顺序。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档的核心观点是，Kafka的顺序保证是基于Partition的。每个Partition可以看作一个有序的、不可变的日志文件，消息按顺序追加。然而，一个Topic通常包含多个Partition以实现高并发和负载均衡，Kafka无法保证在这些不同Partition之间的消息是全局有序的。",
    "source": "文档中「1. Kafka 消息顺序保证概述」和「5. 顺序保证的局限性：多个 Partition」部分",
    "hint": "文档开篇即强调：\"Kafka能保证消息在Partition内的顺序，但不同Partition之间的顺序是无法保证的。\""
  },
  {
    "question": "在Kafka中，如果一个Producer希望确保一系列相关的消息（例如，同一个订单的不同状态更新）被按顺序处理，最核心的机制是什么？",
    "options": [
      "为这些相关的消息设置相同的Key，以确保它们被路由到同一个Partition。",
      "在消息体中加入时间戳，让消费者自行排序。",
      "将这些消息发送到不同的Partition以提高并发性。",
      "使用`ack=all`配置来强制所有消息同步写入。"
    ],
    "correctAnswerIndex": 0,
    "explanation": "文档解释了Producer端的顺序保证机制。Kafka的默认Partitioner会根据消息的Key的哈希值来选择Partition。因此，所有具有相同Key的消息都会被稳定地发送到同一个Partition中。利用Partition内部的有序性，即可保证这些相关消息的处理顺序。",
    "source": "文档中「3. Producer 端的顺序保证」和「6. Kafka 中的顺序消费场景」部分",
    "hint": "思考一下文档中提到的`DefaultPartitioner`是如何利用消息的哪个属性来决定其目的地的。"
  },
  {
    "question": "在Kafka的Partition中，`Offset`的核心作用是什么？",
    "options": [
      "它表示消息的发送时间戳，用于时间排序。",
      "它是消息在整个Topic中的全局唯一ID。",
      "它是一个唯一的标识符，代表消息在Partition内的位置，消费者根据它来顺序消费。",
      "它用于在多个Partition之间同步消息顺序。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档中提到，每个Partition是一个日志文件，而Offset是消息在这个日志文件中的位置信息，可以理解为行号或数组索引。它在Partition内是唯一且单调递增的。消费者正是通过追踪和更新自己消费到的Offset来实现对Partition内消息的顺序读取和断点续传。",
    "source": "文档中「2. Partition 内的顺序保证」和「4. 消费者端的顺序保证」部分",
    "hint": "文档中描述：“每个消息都有一个唯一的Offset，Offset是该消息在Partition内的位置信息...”"
  },
  {
    "question": "Kafka的消费者组（Consumer Group）机制是如何在保证顺序性的同时实现并发消费的？",
    "options": [
      "消费者组会从所有Partition中拉取消息并进行全局排序后才分发给消费者。",
      "在一个消费者组内，每个Partition最多只能被一个消费者实例消费，从而保证了该Partition内消息的消费顺序。",
      "消费者组内的所有消费者共享同一个Offset，以确保全局消费进度同步。",
      "消费者组允许多个消费者同时消费同一个Partition，通过投票机制来决定消息处理顺序。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档指出，消费者组是Kafka实现高吞吐消费的关键。其核心规则是：在一个消费者组内，一个Partition在同一时间只会被分配给一个消费者。这个设计巧妙地将并行处理（多个消费者处理多个Partition）和顺序保证（单个消费者顺序处理其分配到的Partition）结合起来。",
    "source": "文档中「4. 消费者端的顺序保证」部分",
    "hint": "思考一下消费者组中，一个分区（Partition）和消费者（Consumer）之间的分配关系是怎样的。"
  },
  {
    "question": "根据文档，为了保证严格的消息顺序性（例如，将所有相关消息发送到单个Partition），可能会带来什么潜在的性能问题？",
    "options": [
      "会导致Zookeeper的负载过高，引发集群协调问题。",
      "会显著增加消息的存储空间占用。",
      "会强制所有Broker节点进行同步写入，降低写入速度。",
      "可能导致单个Partition的负载过高，形成热点，从而影响整个集群的吞吐量。"
    ],
    "correctAnswerIndex": 3,
    "explanation": "文档在“顺序保证的性能影响”一节中明确指出，如果大量消息因为具有相同的Key而被路由到同一个Partition，该Partition的写入和读取负载会远高于其他Partition。这种数据倾斜会导致该Partition成为整个系统的性能瓶颈，无法充分利用集群的并行处理能力。",
    "source": "文档中「7. 顺序保证的性能影响」部分",
    "hint": "文档中提到了\"Producer端\"的性能开销，思考一下当大量流量集中到一个点时会发生什么。"
  },
  {
    "question": "当一个Partition的Leader节点发生故障时，Kafka会采取什么措施来维护服务的可用性和数据一致性，这与顺序性有什么关系？",
    "options": [
      "Kafka会从ISR(In-Sync Replicas)中选举一个新的Leader，以保护已提交消息的顺序和一致性。",
      "Kafka会立即从所有副本中随机选择一个新Leader，无论其数据是否同步。",
      "Kafka会请求Zookeeper重新创建一个新的空Partition来替代故障的Partition。",
      "Kafka会等待原Leader节点恢复，在此期间该Partition暂停服务。"
    ],
    "correctAnswerIndex": 0,
    "explanation": "文档最后一部分提到，为保证高可用和数据不丢失，Kafka维护了一组与Leader数据保持同步的副本，称为ISR（In-Sync Replicas）。当Leader故障时，控制器会从ISR中选择一个新Leader。因为ISR中的副本拥有最完整的数据，这种机制确保了新的Leader拥有所有已被确认提交的消息，从而维持了Offset的连续性，保护了消息的顺序。",
    "source": "文档中「9. 顺序性在分布式系统中的挑战」部分",
    "hint": "文档中提到“当一个Leader节点宕机时，Kafka会从ISR（In-Sync Replicas）中选择一个新的Leader来恢复服务...”。"
  }
];
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        let currentQuestionIndex = 0;
        let score = 0;
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          optionsListEl.innerHTML = '';
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            nextBtn.style.display = 'block';
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                // 高亮正确答案
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                    child.style.borderColor = '#c3e6cb';
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                // 如果选错了，高亮错误选项
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            document.getElementById('quiz-footer').style.display = 'none';

            const correctCount = score;
            const totalQuestions = quizData.length;
            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
            const accuracy = totalQuestions > 0 ? Math.round((correctCount / totalQuestions) * 100) : 0;
            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

            const finalScoreHtml =
                '<div class="final-score-container">' +
                '<h2>答题报告</h2>' +
                '<div class="score-summary">' +
                '<div class="score-card">' +
                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
                '</div>' +
                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

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        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  